
"""simple docstring"""



import collections
import inspect
import unittest

from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import (
     FocalNetBackbone,
     FocalNetForImageClassification,
     FocalNetForMaskedImageModeling,
     FocalNetModel,
 )
 from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor




class 		a_    :







    def __init__(      self      :							List[Any]  ,       __UpperCamelCase      :							Union[str, Any]  ,       __UpperCamelCase      :							Tuple=13  ,       __UpperCamelCase      :							Any=32  ,       __UpperCamelCase      :							List[str]=2  ,       __UpperCamelCase      :							List[str]=3  ,       __UpperCamelCase      :							Optional[Any]=16  ,       __UpperCamelCase      :							int=[32, 64, 1_28]  ,       __UpperCamelCase      :							str=[1, 2, 1]  ,       __UpperCamelCase      :							Tuple=[2, 2, 4]  ,       __UpperCamelCase      :							Optional[int]=2  ,       __UpperCamelCase      :							int=2.0  ,       __UpperCamelCase      :							Optional[int]=True  ,       __UpperCamelCase      :							int=0.0  ,       __UpperCamelCase      :							List[str]=0.0  ,       __UpperCamelCase      :							int=0.1  ,       __UpperCamelCase      :							str="gelu"  ,       __UpperCamelCase      :							Union[str, Any]=False  ,       __UpperCamelCase      :							Optional[Any]=True  ,       __UpperCamelCase      :							Optional[int]=0.0_2  ,       __UpperCamelCase      :							int=1e-5  ,       __UpperCamelCase      :							Any=True  ,       __UpperCamelCase      :							List[str]=None  ,       __UpperCamelCase      :							Any=True  ,       __UpperCamelCase      :							Any=10  ,       __UpperCamelCase      :							List[Any]=8  ,       __UpperCamelCase      :							Optional[int]=["stage1", "stage2"]  ,       __UpperCamelCase      :							Optional[Any]=[1, 2]  ,       )		->int:






        '''simple docstring'''






        _UpperCAmelCase        = parent
        _UpperCAmelCase        = batch_size
        _UpperCAmelCase        = image_size
        _UpperCAmelCase        = patch_size
        _UpperCAmelCase        = num_channels
        _UpperCAmelCase        = embed_dim
        _UpperCAmelCase        = hidden_sizes
        _UpperCAmelCase        = depths
        _UpperCAmelCase        = num_heads
        _UpperCAmelCase        = window_size
        _UpperCAmelCase        = mlp_ratio
        _UpperCAmelCase        = qkv_bias
        _UpperCAmelCase        = hidden_dropout_prob
        _UpperCAmelCase        = attention_probs_dropout_prob
        _UpperCAmelCase        = drop_path_rate
        _UpperCAmelCase        = hidden_act
        _UpperCAmelCase        = use_absolute_embeddings
        _UpperCAmelCase        = patch_norm
        _UpperCAmelCase        = layer_norm_eps
        _UpperCAmelCase        = initializer_range
        _UpperCAmelCase        = is_training
        _UpperCAmelCase        = scope
        _UpperCAmelCase        = use_labels
        _UpperCAmelCase        = type_sequence_label_size
        _UpperCAmelCase        = encoder_stride
        _UpperCAmelCase        = out_features
        _UpperCAmelCase        = out_indices







    def   _snake_case       (      self      :							Any  )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

        _UpperCAmelCase        = None
        if self.use_labels:
            _UpperCAmelCase        = ids_tensor([self.batch_size]  ,       self.type_sequence_label_size  )

        _UpperCAmelCase        = self.get_config()

        return config, pixel_values, labels







    def   _snake_case       (      self      :							List[Any]  )		->Optional[Any]:






        '''simple docstring'''






        return FocalNetConfig(
            image_size=self.image_size  ,       patch_size=self.patch_size  ,       num_channels=self.num_channels  ,       embed_dim=self.embed_dim  ,       hidden_sizes=self.hidden_sizes  ,       depths=self.depths  ,       num_heads=self.num_heads  ,       window_size=self.window_size  ,       mlp_ratio=self.mlp_ratio  ,       qkv_bias=self.qkv_bias  ,       hidden_dropout_prob=self.hidden_dropout_prob  ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,       drop_path_rate=self.drop_path_rate  ,       hidden_act=self.hidden_act  ,       use_absolute_embeddings=self.use_absolute_embeddings  ,       path_norm=self.patch_norm  ,       layer_norm_eps=self.layer_norm_eps  ,       initializer_range=self.initializer_range  ,       encoder_stride=self.encoder_stride  ,       out_features=self.out_features  ,       out_indices=self.out_indices  ,       )







    def   _snake_case       (      self      :							Union[str, Any]  ,       __UpperCamelCase      :							str  ,       __UpperCamelCase      :							Union[str, Any]  ,       __UpperCamelCase      :							Union[str, Any]  )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = FocalNetModel(config=__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(__UpperCamelCase  )

        _UpperCAmelCase        = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths  ) - 1))
        _UpperCAmelCase        = int(config.embed_dim * 2 ** (len(config.depths  ) - 1)  )

        self.parent.assertEqual(result.last_hidden_state.shape  ,       (self.batch_size, expected_seq_len, expected_dim)  )







    def   _snake_case       (      self      :							List[Any]  ,       __UpperCamelCase      :							str  ,       __UpperCamelCase      :							Dict  ,       __UpperCamelCase      :							Union[str, Any]  )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = FocalNetBackbone(config=__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(__UpperCamelCase  )

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps  )  ,       len(config.out_features  )  )
        self.parent.assertListEqual(list(result.feature_maps[0].shape  )  ,       [self.batch_size, self.image_size, 8, 8]  )

        # verify channels
        self.parent.assertEqual(len(model.channels  )  ,       len(config.out_features  )  )
        self.parent.assertListEqual(model.channels  ,       config.hidden_sizes[:-1]  )

        # verify backbone works with out_features=None
        _UpperCAmelCase        = None
        _UpperCAmelCase        = FocalNetBackbone(config=__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(__UpperCamelCase  )

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps  )  ,       1  )
        self.parent.assertListEqual(list(result.feature_maps[0].shape  )  ,       [self.batch_size, self.image_size * 2, 4, 4]  )

        # verify channels
        self.parent.assertEqual(len(model.channels  )  ,       1  )
        self.parent.assertListEqual(model.channels  ,       [config.hidden_sizes[-1]]  )







    def   _snake_case       (      self      :							int  ,       __UpperCamelCase      :							Dict  ,       __UpperCamelCase      :							Optional[Any]  ,       __UpperCamelCase      :							Any  )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase        = FocalNetForMaskedImageModeling(config=__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(__UpperCamelCase  )
        self.parent.assertEqual(
            result.reconstruction.shape  ,       (self.batch_size, self.num_channels, self.image_size, self.image_size)  )

        # test greyscale images
        _UpperCAmelCase        = 1
        _UpperCAmelCase        = FocalNetForMaskedImageModeling(__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()

        _UpperCAmelCase        = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
        _UpperCAmelCase        = model(__UpperCamelCase  )
        self.parent.assertEqual(result.reconstruction.shape  ,       (self.batch_size, 1, self.image_size, self.image_size)  )







    def   _snake_case       (      self      :							Union[str, Any]  ,       __UpperCamelCase      :							Union[str, Any]  ,       __UpperCamelCase      :							Dict  ,       __UpperCamelCase      :							Optional[Any]  )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase        = self.type_sequence_label_size
        _UpperCAmelCase        = FocalNetForImageClassification(__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(__UpperCamelCase  ,       labels=__UpperCamelCase  )
        self.parent.assertEqual(result.logits.shape  ,       (self.batch_size, self.type_sequence_label_size)  )

        # test greyscale images
        _UpperCAmelCase        = 1
        _UpperCAmelCase        = FocalNetForImageClassification(__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()

        _UpperCAmelCase        = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
        _UpperCAmelCase        = model(__UpperCamelCase  )
        self.parent.assertEqual(result.logits.shape  ,       (self.batch_size, self.type_sequence_label_size)  )







    def   _snake_case       (      self      :							Optional[Any]  )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase        = self.prepare_config_and_inputs()

        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = config_and_inputs
        _UpperCAmelCase        = {"""pixel_values""": pixel_values}
        return config, inputs_dict







@require_torch
class 		a_    (			_UpperCAmelCase  ,    _UpperCAmelCase  ,    unittest.TestCase						):
    a   :					int			     =					(
        (
            FocalNetModel,
            FocalNetForImageClassification,
            FocalNetForMaskedImageModeling,
            FocalNetBackbone,
        )
        if is_torch_available()
        else ()
    )
    a   :					Any			     =					(
        {'feature-extraction': FocalNetModel, 'image-classification': FocalNetForImageClassification}
        if is_torch_available()
        else {}
    )
    a   :					int			     =					False

    a   :					int			     =					False
    a   :					Any			     =					False
    a   :					Any			     =					False
    a   :					Tuple			     =					False







    def   _snake_case       (      self      :							List[Any]  )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = FocalNetModelTester(self  )
        _UpperCAmelCase        = ConfigTester(self  ,       config_class=__UpperCamelCase  ,       embed_dim=37  ,       has_text_modality=__UpperCamelCase  )







    def   _snake_case       (      self      :							Dict  )		->Optional[int]:






        '''simple docstring'''






        self.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()







    def   _snake_case       (      self      :							List[Any]  )		->List[Any]:






        '''simple docstring'''






        return







    def   _snake_case       (      self      :							Union[str, Any]  )		->str:






        '''simple docstring'''






        _UpperCAmelCase        = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCamelCase  )







    def   _snake_case       (      self      :							Optional[int]  )		->int:






        '''simple docstring'''






        _UpperCAmelCase        = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*__UpperCamelCase  )







    def   _snake_case       (      self      :							Union[str, Any]  )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCamelCase  )







    def   _snake_case       (      self      :							Tuple  )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase        = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase  )







    @unittest.skip(reason="""FocalNet does not use inputs_embeds"""  )
    def   _snake_case       (      self      :							Dict  )		->Any:






        '''simple docstring'''






        pass







    @unittest.skip(reason="""FocalNet does not use feedforward chunking"""  )
    def   _snake_case       (      self      :							int  )		->Tuple:






        '''simple docstring'''






        pass







    def   _snake_case       (      self      :							List[str]  )		->Any:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase        = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes[:-1]:
            _UpperCAmelCase        = model_class(__UpperCamelCase  )
            self.assertIsInstance(model.get_input_embeddings()  ,       (nn.Module)  )
            _UpperCAmelCase        = model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(__UpperCamelCase  ,       nn.Linear  )  )







    def   _snake_case       (      self      :							int  )		->int:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase        = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes[:-1]:
            _UpperCAmelCase        = model_class(__UpperCamelCase  )
            _UpperCAmelCase        = inspect.signature(model.forward  )
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            _UpperCAmelCase        = [*signature.parameters.keys()]

            _UpperCAmelCase        = ["""pixel_values"""]
            self.assertListEqual(arg_names[:1]  ,       __UpperCamelCase  )







    def   _snake_case       (      self      :							Any  ,       __UpperCamelCase      :							Optional[Any]  ,       __UpperCamelCase      :							Dict  ,       __UpperCamelCase      :							Optional[int]  ,       __UpperCamelCase      :							Tuple  )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase        = model_class(__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()

        with torch.no_grad():
            _UpperCAmelCase        = model(**self._prepare_for_class(__UpperCamelCase  ,       __UpperCamelCase  )  )

        _UpperCAmelCase        = outputs.hidden_states

        _UpperCAmelCase        = getattr(
            self.model_tester  ,       """expected_num_hidden_layers"""  ,       len(self.model_tester.depths  ) + 1  )
        self.assertEqual(len(__UpperCamelCase  )  ,       __UpperCamelCase  )

        # FocalNet has a different seq_length
        _UpperCAmelCase        = (
            config.patch_size
            if isinstance(config.patch_size  ,       collections.abc.Iterable  )
            else (config.patch_size, config.patch_size)
        )

        _UpperCAmelCase        = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

        self.assertListEqual(
            list(hidden_states[0].shape[-2:]  )  ,       [num_patches, self.model_tester.embed_dim]  ,       )

        _UpperCAmelCase        = outputs.reshaped_hidden_states
        self.assertEqual(len(__UpperCamelCase  )  ,       __UpperCamelCase  )

        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = reshaped_hidden_states[0].shape
        _UpperCAmelCase        = (
            reshaped_hidden_states[0].view(__UpperCamelCase  ,       __UpperCamelCase  ,       height * width  ).permute(0  ,       2  ,       1  )
        )
        self.assertListEqual(
            list(reshaped_hidden_states.shape[-2:]  )  ,       [num_patches, self.model_tester.embed_dim]  ,       )







    def   _snake_case       (      self      :							Optional[int]  )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase        = self.model_tester.prepare_config_and_inputs_for_common()

        _UpperCAmelCase        = (
            self.model_tester.image_size
            if isinstance(self.model_tester.image_size  ,       collections.abc.Iterable  )
            else (self.model_tester.image_size, self.model_tester.image_size)
        )

        for model_class in self.all_model_classes[:-1]:
            _UpperCAmelCase        = True
            self.check_hidden_states_output(__UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  )

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            _UpperCAmelCase        = True

            self.check_hidden_states_output(__UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  )







    def   _snake_case       (      self      :							str  )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase        = self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCAmelCase        = 3

        _UpperCAmelCase        = (
            self.model_tester.image_size
            if isinstance(self.model_tester.image_size  ,       collections.abc.Iterable  )
            else (self.model_tester.image_size, self.model_tester.image_size)
        )
        _UpperCAmelCase        = (
            config.patch_size
            if isinstance(config.patch_size  ,       collections.abc.Iterable  )
            else (config.patch_size, config.patch_size)
        )

        _UpperCAmelCase        = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
        _UpperCAmelCase        = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

        for model_class in self.all_model_classes[:-1]:
            _UpperCAmelCase        = True
            self.check_hidden_states_output(__UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       (padded_height, padded_width)  )

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            _UpperCAmelCase        = True
            self.check_hidden_states_output(__UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       (padded_height, padded_width)  )







    @slow
    def   _snake_case       (      self      :							Union[str, Any]  )		->Union[str, Any]:






        '''simple docstring'''






        for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            _UpperCAmelCase        = FocalNetModel.from_pretrained(__UpperCamelCase  )
            self.assertIsNotNone(__UpperCamelCase  )







    def   _snake_case       (      self      :							int  )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase        = self.model_tester.prepare_config_and_inputs_for_common()

        _UpperCAmelCase        = _config_zero_init(__UpperCamelCase  )
        for model_class in self.all_model_classes:
            _UpperCAmelCase        = model_class(config=__UpperCamelCase  )
            for name, param in model.named_parameters():
                if "embeddings" not in name and param.requires_grad:
                    self.assertIn(
                        ((param.data.mean() * 1e9).round() / 1e9).item()  ,       [0.0, 1.0]  ,       msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""  ,       )







@require_vision
@require_torch
class 		a_    (			unittest.TestCase						):







    @cached_property
    def   _snake_case       (      self      :							Any  )		->Tuple:






        '''simple docstring'''






        return AutoImageProcessor.from_pretrained("""microsoft/focalnet-tiny"""  ) if is_vision_available() else None







    @slow
    def   _snake_case       (      self      :							Tuple  )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase        = FocalNetForImageClassification.from_pretrained("""microsoft/focalnet-tiny"""  ).to(__UpperCamelCase  )
        _UpperCAmelCase        = self.default_image_processor

        _UpperCAmelCase        = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )
        _UpperCAmelCase        = image_processor(images=__UpperCamelCase  ,       return_tensors="""pt"""  ).to(__UpperCamelCase  )

        # forward pass
        with torch.no_grad():
            _UpperCAmelCase        = model(**__UpperCamelCase  )

        # verify the logits
        _UpperCAmelCase        = torch.Size((1, 10_00)  )
        self.assertEqual(outputs.logits.shape  ,       __UpperCamelCase  )
        _UpperCAmelCase        = torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1]  ).to(__UpperCamelCase  )
        self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,       __UpperCamelCase  ,       atol=1e-4  )  )
        self.assertTrue(outputs.logits.argmax(dim=-1  ).item()  ,       2_81  )







@require_torch
class 		a_    (			_UpperCAmelCase  ,    unittest.TestCase						):
    a   :					Optional[Any]			     =					(FocalNetBackbone,) if is_torch_available() else ()
    a   :					Optional[Any]			     =					FocalNetConfig

    a   :					Optional[int]			     =					False







    def   _snake_case       (      self      :							Dict  )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = FocalNetModelTester(self  )

"""simple docstring"""



import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


a       :						List[str]       		=		logging.get_logger(__name__)




class 		a_    :







    def __init__(      self      :							List[str]  ,       __UpperCamelCase      :							Any  ,       __UpperCamelCase      :							Tuple  )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase        = question_encoder
        _UpperCAmelCase        = generator
        _UpperCAmelCase        = self.question_encoder







    def   _snake_case       (      self      :							int  ,       __UpperCamelCase      :							str  )		->Dict:






        '''simple docstring'''






        if os.path.isfile(__UpperCamelCase  ):
            raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""  )
        os.makedirs(__UpperCamelCase  ,       exist_ok=__UpperCamelCase  )
        _UpperCAmelCase        = os.path.join(__UpperCamelCase  ,       """question_encoder_tokenizer"""  )
        _UpperCAmelCase        = os.path.join(__UpperCamelCase  ,       """generator_tokenizer"""  )
        self.question_encoder.save_pretrained(__UpperCamelCase  )
        self.generator.save_pretrained(__UpperCamelCase  )







    @classmethod
    def   _snake_case       (      cls      :							Optional[Any]  ,       __UpperCamelCase      :							Tuple  ,       **__UpperCamelCase      :							Optional[Any]  )		->Tuple:






        '''simple docstring'''






        from ..auto.tokenization_auto import AutoTokenizer

        _UpperCAmelCase        = kwargs.pop("""config"""  ,       __UpperCamelCase  )

        if config is None:
            _UpperCAmelCase        = RagConfig.from_pretrained(__UpperCamelCase  )

        _UpperCAmelCase        = AutoTokenizer.from_pretrained(
            __UpperCamelCase  ,       config=config.question_encoder  ,       subfolder="""question_encoder_tokenizer"""  )
        _UpperCAmelCase        = AutoTokenizer.from_pretrained(
            __UpperCamelCase  ,       config=config.generator  ,       subfolder="""generator_tokenizer"""  )
        return cls(question_encoder=__UpperCamelCase  ,       generator=__UpperCamelCase  )







    def __call__(      self      :							Any  ,       *__UpperCamelCase      :							List[Any]  ,       **__UpperCamelCase      :							Optional[Any]  )		->Optional[Any]:






        '''simple docstring'''






        return self.current_tokenizer(*__UpperCamelCase  ,       **__UpperCamelCase  )







    def   _snake_case       (      self      :							Union[str, Any]  ,       *__UpperCamelCase      :							List[Any]  ,       **__UpperCamelCase      :							Tuple  )		->Any:






        '''simple docstring'''






        return self.generator.batch_decode(*__UpperCamelCase  ,       **__UpperCamelCase  )







    def   _snake_case       (      self      :							Optional[int]  ,       *__UpperCamelCase      :							Optional[int]  ,       **__UpperCamelCase      :							List[str]  )		->Union[str, Any]:






        '''simple docstring'''






        return self.generator.decode(*__UpperCamelCase  ,       **__UpperCamelCase  )







    def   _snake_case       (      self      :							Union[str, Any]  )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = self.question_encoder







    def   _snake_case       (      self      :							Tuple  )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase        = self.generator







    def   _snake_case       (      self      :							List[Any]  ,       __UpperCamelCase      :							List[str]  ,       __UpperCamelCase      :							Optional[List[str]] = None  ,       __UpperCamelCase      :							Optional[int] = None  ,       __UpperCamelCase      :							Optional[int] = None  ,       __UpperCamelCase      :							str = "longest"  ,       __UpperCamelCase      :							str = None  ,       __UpperCamelCase      :							bool = True  ,       **__UpperCamelCase      :							Tuple  ,       )		->BatchEncoding:






        '''simple docstring'''






        warnings.warn(
            """`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the """
            """regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` """
            """context manager to prepare your targets. See the documentation of your specific tokenizer for more """
            """details"""  ,       __UpperCamelCase  ,       )
        if max_length is None:
            _UpperCAmelCase        = self.current_tokenizer.model_max_length
        _UpperCAmelCase        = self(
            __UpperCamelCase  ,       add_special_tokens=__UpperCamelCase  ,       return_tensors=__UpperCamelCase  ,       max_length=__UpperCamelCase  ,       padding=__UpperCamelCase  ,       truncation=__UpperCamelCase  ,       **__UpperCamelCase  ,       )
        if tgt_texts is None:
            return model_inputs
        # Process tgt_texts
        if max_target_length is None:
            _UpperCAmelCase        = self.current_tokenizer.model_max_length
        _UpperCAmelCase        = self(
            text_target=__UpperCamelCase  ,       add_special_tokens=__UpperCamelCase  ,       return_tensors=__UpperCamelCase  ,       padding=__UpperCamelCase  ,       max_length=__UpperCamelCase  ,       truncation=__UpperCamelCase  ,       **__UpperCamelCase  ,       )
        _UpperCAmelCase        = labels["""input_ids"""]
        return model_inputs